#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
MCP (Model Control Protocol) 集成模块
结合智源研究院数据和文心大模型的多模态能力
"""

import json
import asyncio
from typing import Dict, List, Any, Optional
import logging

class MCPIntegration:
    """MCP集成类，处理模型控制协议相关功能"""
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        self.zhiyuan_data = {}
        self.wenxin_client = None
        
    async def initialize(self):
        """初始化MCP连接和智源数据"""
        try:
            await self.load_zhiyuan_data()
            await self.setup_wenxin_connection()
            self.logger.info("MCP集成初始化完成")
        except Exception as e:
            self.logger.error(f"MCP初始化失败: {e}")
            
    async def load_zhiyuan_data(self):
        """加载智源研究院数据"""
        # 模拟智源数据加载
        self.zhiyuan_data = {
            "educational_concepts": {
                "数学": ["加法", "减法", "乘法", "除法", "几何"],
                "科学": ["物理", "化学", "生物", "地理"],
                "语言": ["拼音", "汉字", "词汇", "语法"],
                "艺术": ["绘画", "音乐", "舞蹈", "手工"]
            },
            "cognitive_development": {
                "3-5岁": ["基础认知", "简单逻辑", "语言发展"],
                "6-8岁": ["抽象思维", "数学概念", "阅读理解"],
                "9-12岁": ["复杂推理", "科学探索", "创造性思维"]
            }
        }
        
    async def setup_wenxin_connection(self):
        """设置文心大模型连接"""
        # 这里应该连接到实际的文心API
        self.wenxin_client = {
            "api_key": "your_wenxin_api_key",
            "endpoint": "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie-4.0-8k"
        }
        
    async def generate_educational_content(self, topic: str, age_group: str) -> Dict[str, Any]:
        """基于智源数据生成教育内容"""
        try:
            # 获取相关的教育概念
            relevant_concepts = self.get_relevant_concepts(topic, age_group)
            
            # 使用文心大模型生成内容
            content = await self.wenxin_generate(topic, relevant_concepts, age_group)
            
            return {
                "success": True,
                "content": content,
                "concepts": relevant_concepts,
                "age_appropriate": True
            }
        except Exception as e:
            self.logger.error(f"生成教育内容失败: {e}")
            return {"success": False, "error": str(e)}
            
    def get_relevant_concepts(self, topic: str, age_group: str) -> List[str]:
        """获取相关的教育概念"""
        concepts = []
        
        # 从智源数据中提取相关概念
        for category, concept_list in self.zhiyuan_data["educational_concepts"].items():
            if topic.lower() in category.lower():
                concepts.extend(concept_list)
                
        # 根据年龄组过滤概念
        age_concepts = self.zhiyuan_data["cognitive_development"].get(age_group, [])
        concepts.extend(age_concepts)
        
        return list(set(concepts))
        
    async def wenxin_generate(self, topic: str, concepts: List[str], age_group: str) -> str:
        """使用文心大模型生成内容"""
        # 构建提示词
        prompt = f"""
        基于以下信息生成适合{age_group}儿童的教育故事内容：
        主题：{topic}
        相关概念：{', '.join(concepts)}
        
        要求：
        1. 语言简单易懂
        2. 富有想象力和趣味性
        3. 包含教育意义
        4. 适合年龄特点
        """
        
        # 模拟文心API调用
        # 实际应用中应该调用真实的文心API
        generated_content = f"""
        在一个神奇的{topic}王国里，住着一群可爱的小动物。
        它们每天都在学习新的知识，比如{concepts[0] if concepts else '有趣的事物'}。
        
        有一天，小兔子发现了一个秘密...
        """
        
        return generated_content
        
    async def process_multimodal_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """处理多模态输入（文本、图像、语音）"""
        try:
            result = {"success": True, "processed_data": {}}
            
            if "text" in input_data:
                result["processed_data"]["text"] = await self.process_text(input_data["text"])
                
            if "image" in input_data:
                result["processed_data"]["image"] = await self.process_image(input_data["image"])
                
            if "audio" in input_data:
                result["processed_data"]["audio"] = await self.process_audio(input_data["audio"])
                
            return result
        except Exception as e:
            return {"success": False, "error": str(e)}
            
    async def process_text(self, text: str) -> Dict[str, Any]:
        """处理文本输入"""
        return {
            "original": text,
            "processed": text.strip(),
            "sentiment": "positive",  # 模拟情感分析
            "keywords": text.split()[:5]  # 模拟关键词提取
        }
        
    async def process_image(self, image_data: bytes) -> Dict[str, Any]:
        """处理图像输入"""
        return {
            "size": len(image_data),
            "format": "detected",
            "objects": ["模拟检测到的物体"],  # 模拟物体检测
            "description": "这是一张包含教育元素的图片"
        }
        
    async def process_audio(self, audio_data: bytes) -> Dict[str, Any]:
        """处理音频输入"""
        return {
            "duration": 10.5,  # 模拟音频时长
            "transcription": "模拟语音转文字结果",
            "emotion": "happy",  # 模拟情感识别
            "language": "zh-CN"
        }
        
    async def enhance_story_with_knowledge(self, story_content: str, topic: str) -> str:
        """使用智源知识库增强故事内容"""
        try:
            # 获取相关的知识点
            knowledge_points = self.get_knowledge_points(topic)
            
            # 将知识点融入故事
            enhanced_story = self.integrate_knowledge(story_content, knowledge_points)
            
            return enhanced_story
        except Exception as e:
            self.logger.error(f"知识增强失败: {e}")
            return story_content
            
    def get_knowledge_points(self, topic: str) -> List[Dict[str, str]]:
        """从智源数据获取知识点"""
        knowledge_points = []
        
        # 模拟知识点提取
        if "数学" in topic:
            knowledge_points = [
                {"concept": "数字概念", "explanation": "数字是用来计数和测量的符号"},
                {"concept": "基础运算", "explanation": "加法是把数量合并在一起"}
            ]
        elif "科学" in topic:
            knowledge_points = [
                {"concept": "观察", "explanation": "用眼睛仔细看周围的事物"},
                {"concept": "实验", "explanation": "通过动手操作来验证想法"}
            ]
            
        return knowledge_points
        
    def integrate_knowledge(self, story: str, knowledge_points: List[Dict[str, str]]) -> str:
        """将知识点集成到故事中"""
        enhanced_story = story
        
        for point in knowledge_points:
            # 在故事中自然地插入知识点
            knowledge_insert = f"\n\n💡 小知识：{point['explanation']}"
            enhanced_story += knowledge_insert
            
        return enhanced_story

# 全局MCP实例
mcp_instance = MCPIntegration()

async def initialize_mcp():
    """初始化MCP集成"""
    await mcp_instance.initialize()
    
def get_mcp_instance() -> MCPIntegration:
    """获取MCP实例"""
    return mcp_instance